A number of techniques are known for adjusting the contrast of an image, particularly in order to improve the contrast and therefore visibility of the image. Contrast adjustment or enhancement of images, particularly digital images, is used in many fields, including enhancing the contrast of a digital image for display by a television receiver or other display device, for printing by a printer, in digital cameras, etc., etc. Contrast enhancement is used to improve the contrast in medical and other images.
Global contrast enhancement techniques remedy problems that manifest themselves in a global fashion, such as excessive or poor lighting conditions in the source environment. On the other hand, local contrast enhancement attempts to enhance the visibility of local details in the image.
A particular known local contrast enhancement method is adaptive contrast enhancement. An example is disclosed in “Digital Image Enhancement and Noise Filtering by Using Local Statistics”, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-2, pp. 165-168, February 1980, by J. S. Lee. The method disclosed in this paper is a modified form of unsharp masking technique. The method of this paper is shown schematically in FIG. 1. An input image 1 is filtered by a low pass filter 2, which effectively removes the “sharp” or high frequency component of the input image 1 to leave an unsharp or low frequency image. This unsharp or low frequency image is subtracted from the original image in a summer 3 to obtain the high frequency component of the input image 1. This high frequency component is amplified with a gain in an amplifier 4 and then added back to the input image 1 in a second summer 5 to produce the enhanced output image. However, this particular method amplifies the high frequency component with a fixed gain factor. This causes ringing or over/under shoot effects around the edges or other regions of high contrast in the original image because of the high values of the high frequency component around the edges after the amplification.
A modified version of this adaptive contrast enhancement technique is disclosed in “Real-time Adaptive Contrast Enhancement”, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, pp. 655-661, June 1981, by P. M. Narendra and R. C. Fitch. FIG. 2 shows schematically the method of this paper. The gain that is used to amplify the high frequency component is made to be inversely proportional to the local variance in the high frequency component. This modified method therefore adapts the spatial gain applied to the high frequency component according to local statistics in the high frequency component. However, this causes the gain to become very large when the local variance is small, which leads to noise amplification in smooth (low contrast) areas of the input image 1.
A further modification of the adaptive contrast enhancement techniques is disclosed in “Image Enhancement via Adaptive Unsharp Masking”, IEEE Trans. On Image Processing, vol. 9, no. 3, March 2000, by A. Polesel, G. Ramponi and V. J. Matthews. In this modified technique, an adaptive filter is used to emphasise the medium contrast details in the image more than large contrast regions such as edges. However, the filter that is disclosed in this paper is a Laplacian filter which therefore has three tap coefficients and which is therefore computationally complex. As disclosed, the method of this paper requires 17 multiplications and one division operation to compute the output brightness level data for each pixel.